The Infogain interview Q&A covers PySpark aspects like partitioning, bucketing, reading petabyte-size files, Delta Lake, & schema-less files.
This PySpark guide covers skipping rows (beyond header) and counting NULLs for each column of a DataFrame.
The article covers PySpark’s Explode, Collect_list, and Anti_join functions, providing code examples and their respective outputs.
If you mistakenly alter a Delta Lake table in Databricks, use time travel to retrieve a previous version.
The examples showcase Python operations, including string manipulation, list sorting, and character counting, providing practical techniques for beginners.
To share a Databricks workspace, manage access controls and permissions for users and groups in various ways.
ROW and RANGE window functions in SQL operate within a window, but differ in row versus value consideration.
In Databricks, managed tables store and manages both data and metadata, while external tables store data externally and manage only metadata.
Writing DataFrames to Delta tables offers data persistence, optimized performance, schema enforcement, transactional consistency, and integration with data systems.
PySparkDatabricks Utilities (DBUtils) provides functionalities like accessing DBFS files, managing clusters, and widgets.
The SQL query, Pandas, and PySpark code extract employees earning more than the average salary.
The PySpark code demonstrates two methods to compare employee salaries with their manager’s and retrieve the results.
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